Nonparametric Method
Candlefocus EditorOne of the main advantages of using nonparametric methods for analysis is that they make fewer assumptions about the data and therefore require fewer parameter restrictions to be placed on the data. This allows for the analysis to be more robust since the data can vary within a broad range while still uncovering important trends and relationships that may not be as apparent when traditional parametric methods are used.
For example, when trying to find which factors explain why some people are admitted to college and others are not, a nonparametric method such as logistic regression can be used to identify the relationship between student attributes and admission decisions. The nonparametric method would not need to assume the data follows a normal distribution, or that the student attributes are related to the admission decisions solely in a linear way. Similarly, one can use Kendall’s Tau coefficient to measure the strength of an association between two ordinal variables without positing a mechanism for how the relationship works.
Nonparametric methods are also useful for comparing data when the data does not fit into a particular distribution, such as when data points have outliers or have been collected from multiple sources. An example of a nonparametric test commonly used for such tasks is the Mann-Whitney U test. This test helps compare two groups of data without making assumptions about the shape of the distribution.
Overall, the nonparametric method is important for data analysis when the data does not fit into a model that has been predetermined by only a few parameters. This method is more robust and flexible than parametric methods, as it requires fewer requirements to be placed on the data in order to uncover relationships between variables. Nonparametric methods are useful when trying to determine the order of trends or when data points have outliers or have been collected from different sources.